Functional Imaging Reconstruction for Small High-Contrast Inclusion Localization
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Solution Overview
Problem
Current nonlinear functional imaging modalities, such as DOT, MWT, and EIT, face challenges in achieving high spatial resolution due to ill-posed inverse problems, leading to low-resolution images and inability to accurately localize small, high-contrast tumors without structural priors.
Innovation Solution
A method that creates a map of physiological properties using functional imaging techniques, dividing the tissue area into regions and applying a reconstruction model with hypothesized physiological properties to determine the location of inclusions without structural images, enhancing contrast and allowing for the characterization of tumors using surrogate metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If regularization is applied to solve the ill-posed inverse problem, then image stability is improved, but spatial resolution is lost
Solution Approach 1:
The patent segments the imaging domain into a small number of discrete regions of interest (ROIs) rather than attempting to reconstruct all pixels uniformly. This segmentation allows the application of regularization to be focused only on determining which ROIs contain inclusions, rather than regularizing the entire image domain. Consequently, spatial resolution within the identified ROIs is preserved while still achieving image stability through regularization.
Solution Approach 2:
The patent applies different reconstruction strategies to different regions of the image. In regions identified as containing inclusions (through the ROI selection process), high spatial resolution is maintained by using minimal regularization. In background regions, full regularization is applied to ensure stability. This local differentiation resolves the contradiction by allowing high resolution where needed while maintaining overall stability.
2Manufacturing precision
If high uniform resolution reconstruction is performed across the entire imaging domain, then spatial detail is improved, but the problem becomes severely under-determined
Solution Approach 1:
The patent extracts and focuses computational resources on a small subset of critical regions (ROIs) that are suspected to contain inclusions. By taking out these specific regions from the full imaging domain and treating them separately, the method reduces the number of unknowns from millions of pixels to a manageable number of ROI locations and parameters. This extraction resolves the under-determination problem while maintaining high spatial resolution within the extracted regions.
Solution Approach 2:
The patent transitions from a pixel-by-pixel reconstruction approach (2D spatial domain) to an ROI-based approach that adds a hierarchical dimension. First, ROIs are identified at a coarse level, then detailed reconstruction is performed only within those ROIs. This dimensional change from uniform fine-grained reconstruction to hierarchical coarse-then-fine reconstruction reduces the overall complexity while preserving local spatial resolution.
3Measurement precision
If measurements are increased to improve reconstruction accuracy, then data redundancy is reduced, but the effective independent information remains limited by the effective rank
Solution Approach 1:
The patent performs preliminary action by identifying and selecting ROIs before performing the detailed reconstruction. This preliminary step uses the measurement data to determine which regions are most likely to contain inclusions, based on the limited independent information available. By performing this preliminary ROI selection first, the method maximizes the use of effective independent measurements to guide subsequent reconstruction, avoiding the waste of computational resources on regions without inclusions.
Data Source
AI summary
A detector and inclusion location method that uses a reconstruction technique to target and localize sparse small-sized but high-contrast objects, such as a tumor inside tissue. The reconstruction technique applied, can dramatically enhance the property contrast of the tumors or abnormal inclusions by ten to one hundred fold. The reconstruction technique enables the use of nonlinear imaging ill-posed techniques that are function-oriented imaging techniques without any need for structural prior knowledge.